Recently, many studies attempt to apply these techniques in computer vision tasks, such as scribble and bounding box-based weakly semantic segmentation [1]}, image-text matching [2]}, visual question answering [3]}, semantic object parsing [4]} and human-object interaction detection [5]}. The most relevant work is [1]}, which uses GCN to deal with scribble and bounding box-based weakly semantic segmentation. However, our approach is fundamentally different from [1]}. Directly extending [1]} does not deal with image-level annotations. The reason is that GCN heavily relies on the supervision quality. But the generated labels by CAM contain a large number of noise compared with scribble and bounding box, which leads to poor performance. Therefore, one key contribution of our work is to detect clean labels from the perspective of learning with noisy labels, which is the crucial foundation of seed-and-expansion strategies.
